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Websites, search engines, recommender systems, artificial intelligence and digital libraries have the potential to support serendipity for unexpected interaction with information and ideas which would lead to favored information discoveries. This paper aims to explore the current state of research into serendipity particularly related to information encountering.

This study provides bibliometric review of 166 studies on serendipity extracted from the Web of Science. Two bibliometric analysis tools HisCite and RStudio (Biblioshiny) are used on 30 years of data. Citation counts and bibliographic records of the papers are assessed using HisCite. Moreover, visualization of prominent sources, countries, keywords and the collaborative networks of authors and institutions are assessed using RStudio (Biblioshiny) software. A total of 166 papers on serendipity were found from the period 1989 to 2022, and the most influential authors, articles, journals, institutions and countries among these were determined.

The highest numbers of 11 papers were published in the year 2019. Makri and Erdelez are the most influential authors for contributing studies on serendipity. “Journal of Documentation” is the top-ranking journal. University College London is the prominent affiliation contributing highest number of studies on serendipity. The UK and the USA are the prominent nations contributing highest number of research. Authorship pattern for research on serendipity reveals involvement of single author in majority of the studies. OA Green model is the most preferred model for archiving of research articles by the authors who worked on serendipity. In addition, majority of the research outputs have received a citation ranging from 0 to 50.

To the best of the authors’ knowledge, this paper may be the first bibliometric analysis on serendipity research using bibliometric tools in library and information science studies. The paper would definitely open new avenues for other serendipity researchers.

Data used to develop the case included primary data from employees and supervisors of a commercial floorcovering manufacturing plant in Northwest Georgia. The case company is not disguised.

The survey was developed using existing instruments from the Organizational Behavior and Human Resources Literature. Instruments were listed in Exhibits 2 through 7. The survey administration had the support of the Vice President for Resources and Facilities, and employees and their supervisors were given time to complete the surveys. The data gathered was analyzed by the researcher using SPSS statistical software.

Established in 1957, J&J started as a family-owned business but had grown and diversified its product offerings by focusing on commercial flooring. It survived several economic downturns and remained competitive in a market dominated by more prominent flooring manufacturers. J&J Industries strived to empower its 800 employees with various incentive programs. Employees remained loyal to J&J; many had worked for the company for over 15 years. However, management wanted to measure the impact of empowering and initiatives on employee performance and satisfaction to determine the real power of employee incentive programs. The Resources and Facilities Vice President employed Professor Lopez, a Management Professor, to develop a survey to measure these constructs and analyze the data to guide future incentive programs. Data from the employee and supervisor survey was provided along with the statistical analysis results for interpretation and recommendations for VP Fordham.

The target audience for this case is primarily students in a research methodology course and students studying quantitative regression analysis and interpretation. The focus is predominantly on graduate-level students in Master of Business Administration or Master of Accounting programs in business. Graduate students should have completed courses in management or organizational behavior, business statistics or quantitative methods or data visualization and cleaning as background knowledge for this case. Specifically, students should understand regression analysis and know when and how the tool is used for managerial decision-making.

Academic integrity in tertiary education is a global concern. This chapter describes academic integrity in Australian universities and proposes an “it takes a village” framework to guide universities toward a re-evaluation of academic integrity education. It takes a village to raise a child – a child needs role models and positive influences from multiple people for healthy growth and development. With regard to academic integrity, the parallel is that the entire university community needs to be involved to foster development of students of integrity. The institution and its community need to provide structures, multiple positive and effective learning experiences, and clear guidelines to support both staff and students. In this chapter, we argue that academic integrity needs to be seen as a complex system, one in which everyone involved has responsibility to develop and maintain a culture of integrity and one which supports a student throughout their academic journey.

Managing mega infrastructure projects (MIPs) is more complex because of time, size, social, environmental and financial implications. This study aims to address the management approaches, complexity and risk factors involved in MIPs. The study focuses on project success criteria and their individual effects on the success of MIPs.

To address the challenges and identify the most influencing factor for the success of MIPs, the study deployed a cross-sectional survey approach. Six hundred eighty-two usable samples were collected from the respondents to understand the impact of predetermined factors on the success of MIPs. The structural equation model and artificial neural network approach were used to derive the importance of factors affecting the success of MIPs.

The study's outcome confirms that all three influencing factors: feasibility studies, community engagements and contract selection, have a significant positive impact on the success of MIPs. Community engagement amongst all three has the most influential predictor for the success of MIPs.

The developed model will enable practitioners and policymakers from Indian construction companies and other emerging nations to concentrate on recognized risk reduction variables to enhance project success criteria and project management success, especially for MIPs.

This study aims to use a theoretical-based case study of two distinct ownership groups of the Jack Daniel’s brand to explore how rhetorical history (i.e. malleability of the past for strategic goals) may evoke and capitalize on different forms of nostalgia. Within, the authors configure four forms of nostalgia (i.e. personal, historical, collective and cultural) from the individual or collective interaction and level of direct experience one has with the past as lived or happened.

This study uses an historical research approach which involved the identification of primary and secondary sources, facility tour, source criticism and triangulation to create themes of rhetorical history infused with nostalgic narratives using compelling evidence through rich description of this fusion.

The findings reveal how nostalgia-driven narratives reflecting different collective longing for the re-creation of an American Paradise Lost used by Jack Daniel (i.e. the man) and later but differently by Brown-Forman. This study uncovers how the company’s inherited past was used rhetorically throughout its history, beginning with the nostalgic story of Jack Daniel and the distillery’s nostalgically choreographed location in Lynchburg, Tennessee. This study delves into this setting to highlight the importance of symbols, details, emotional appeals and communications for collective memory and identity development and to showcase the ways in which they are influenced by different types and forms of nostalgia.

This study adds to a limited number of studies focused on understanding the impact of founders on an organization’s brand and how that is malleable. This study responds to scholarly calls to study the influence of sequenced historical rhetoric on an organization and highlight the relevance of social emotions such as nostalgia for rhetorical history. Finally, the theoretical contribution involves the advancing and construction of a theory typology of nostalgia previously proposed by Havlena and Holak in 1996.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

To provide insights into the role of formal and informal accounts in preventing the liquidation of a professional football club and in post-crisis rebuilding.

This case study, framed as a conflict arena, covers an eight-year period of a high-profile struggle over the future of a professional football club. It uses a mixed methods design, including direct engagement with key actors involved in administration proceedings and transformation to a hybrid supporter-owned organisation.

Our findings suggest that within the arena:• formal accounting and governance were of limited use in managing the complex network of relationships and preventing the abuse of power or existential crises. • informal accounting helped mobilise critical resources and maintain supporters’ emotional investment during periods of conflict. • informal accounts enabled both resistance and coalition-building in response to perceived abuse of power. • informal accounts were used by the Club as part of its legitimation activities.

This study provides theoretical and empirical insights into an unfolding crisis with evidence gathered directly from actors involved in the process. The conceptual framework developed in this paper creates new visibilities and possibilities for developing more effective accounting practices in settings that enable continuing emotional investment from supporters.

Given the ever-growing fiscal commitments of Nigeria and her chequered history of electricity generation and distribution, the fortunes of the energy sector in the country have been affected by the prevalence of energy poverty. Government policies such as public capital expenditure (PCE) present a crucial option for reducing energy poverty in Nigeria, providing the purpose of this study.

To investigate the relationship between government capital spending and five distinct energy poverty proxies, this research applies the Bayer–Hanck cointegration system and the auto-regressive distributed lag (ARDL) bound test.

The findings indicate that public capital spending in Nigeria worsens energy poverty by reducing access to electricity, urban electrification, renewable energy consumption and renewable electricity generation, with a positive but insignificant influence on rural electrification.

This inquiry presents a pioneering investigation of the nexus between PCE and energy poverty in Nigeria. Also, aside from the variables of energy poverty adopted by existing studies, this study incorporates renewable energy consumption and renewable electricity output with implications for energy poverty and sustainable development.

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.

The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.

The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.

This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.

The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector